We assessed the pan-cancer predictability of multi-omic biomarkers from haematoxylin and eosin (H&E)-stained whole slide image (WSI) using deep learning and standard evaluation measures throughout a systematic study. A total of 13,443 deep learning (DL) models predicting 4,481 multi-omic biomarkers across 32 cancer types were trained and validated. The investigated biomarkers included genetic mutations, transcriptomic (mRNA) and proteomic under- and over-expression status, metabolomic pathways, established markers relevant for prognosis, including gene expression signatures, molecular subtypes, clinical outcomes and response to treatment. Overall, we established the general feasibility of predicting multi-omic markers across solid cancer types, where 50% of the models could predict biomarkers with the area under the curve (AUC) of more than 0.633 (with 25% of the models having AUC larger than 0.711). Aggregating across the omic types, our deep learning models achieved the following performance: mean AUC of 0.634 ±0.117 in predicting driver SNV mutations; 0.637 ±0.108 for over-/under-expression of transcriptomic genes; 0.666 ±0.108 for over-/under-expression of proteomes; 0.564 ±0.081 for metabolomic pathways; 0.653 ±0.097 for gene signatures and molecular subtypes; 0.742 ±0.120 for standard of care biomarkers; and 0.671 ±0.120 for clinical outcomes and treatment responses. The biomarkers were shown to be detectable from routine histology images across all investigated cancer types, with aggregate mean AUC exceeding 0.62 in almost all cancers. In addition, we observed that predictability is reproducible within-marker and less dependent on sample size and positivity ratio, indicating a degree of true predictability inherent to the biomarker itself.

We present a public validation of PANProfiler (ER, PR, HER2), an in-vitro medical device (IVD) that predicts the qualitative status of estrogen receptor (ER), progesterone receptor (PR) and human epidermal growth factor receptor 2 (HER2) by analysing the hematoxylin and eosin (H&E)-stained tissue scan. In public validation on 648 (ER), 648 (PR) and 560 (HER2) unseen cases with known biomarker status, the device achieves an accuracy of 87% (ER), 83% (PR) and 87% (HER2). The validation offers early evidence of the ability to predict clinically relevant breast biomarkers from an H&E slide in a relevant clinical setting.